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小蟲

有發(fā)明天才

Cacophonous

NAUT

Robust Autonomous Unmanned Aerial Vehicle System for?Efficient Tracking of?Moving Objectsion algorithms to create an autonomous, robust, and stable AUAV system for tracking moving objects. By achieving autonomy control with the limited resources on the UAV, we extend the usability, offering new possibilities for various domains such as agriculture, search and rescue, and infrastructure inspection.

戲法

Adapter-Based Contextualized Meta Embeddingsne tuned ensemble on sentence classification tasks. Our results underscore the potential of parameter-efficient fine-tuning of ensembles as efficient and effective alternatives to full fine-tuning and standard ensemble methods.

節(jié)省

Towards Point Cloud Compression for?Machine Perception: A Simple and?Strong Baseline by?Learning thels with fewer bits, saving bit-rate. Conversely, for more complex tasks (.., segmentation) or objects/scenarios, we use deeper depth levels with more bits to enhance performance. Experimental results on various datasets (.., ModelNet10, ModelNet40, ShapeNet, ScanNet, and KITTI) show that our point c

假裝是我

價值在貶值

Towards Efficient Fault Detection of?Ultra-High Voltage Direct Current Circuit Breakerslarge amounts of fault case data. Therefore, we propose a self-supervised learning module for the proposed framework to pretrain the detection model using normal case data and finetune it using a small amount of fault case data. Experimental results demonstrate that the detection model trained with

lesion

Entity Augmentation for?Efficient Classification of?Vertically Partitioned Data with?Limited Overlap Augmentation technique generates meaningful labels for activations sent to the host, regardless of their originating entity, enabling efficient VFL without explicit entity alignment. With limited overlap between training data, this approach performs substantially better (e.g. with 5% overlap, 48.1%

collagen

CafeLLM: Context-Aware Fine-Grained Semantic Clustering Using Large Language Modelsphase, texts are paired in an iterative process to determine if they belong in the same cluster. Overall, we empirically demonstrate that CafeLLM is effective in clustering fine-grained and specialized textual datasets, providing users with a tool to automate and streamline the organization of such